Exploiting "quantum-like Interference" In Decision Fusion For Ranking Multimodal Documents

Abstract

Fusing and ranking multimodal information remains always a challenging task. A robust decision-level fusion method should not only be dynamically adaptive for assigning weights to each representation but also incorporate inter-relationships among different modalities. In this paper, we propose a quantum-inspired model for fusing and ranking visual and textual information accounting for the dependency between the aforementioned modalities. At first, we calculate the text-based and image-based similarity individually. Two different approaches have been applied for computing each unimodal similarity. The first one makes use of the bag-of-words model. For the second one, a pre-trained VGG19 model on ImageNet has been used for calculating the image similarity, while a query expansion approach has been applied to the text-based query for improving the retrieval performance. Afterward, the local similarity scores fit the proposed quantum-inspired model. The inter-dependency between the two mo

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